Clawdemy Lessons

Overview of the limit concept, the plain-English epsilon-delta idea, and L'Hopital's rule for 0/0 and infinity/infinity forms, with worked examples.

What the integration lesson covers: the definite integral as Riemann sums, the fundamental theorem, antiderivatives reversed, plus prerequisites and timing.

A guide to implicit differentiation: what it builds, where it fits after the chain rule, prerequisites, and the learning outcomes before you start.

A guided overview of the circle-area derivation: learning outcomes, where it fits in the track, prerequisites (none), and time and difficulty.

Overview of the chain rule: what nested functions are, why rates multiply through a composition, the evaluated-at gotcha, and the link to backpropagation.

What the opening statistics-for-AI lesson covers and why it comes first: the probability-statistics split, the track map, prerequisites, and time.

A preview of the correlation lesson: reading scatterplots, the correlation coefficient, the linear-only limit, correlation versus causation, and ML links.

Overview of the histogram lesson: what you will learn, where it fits after center and spread, the prerequisites, and why reading the shape of data matters.

A tour of the normal distribution lesson: what it covers, where it fits, prerequisites, the light arithmetic, and the AI connections.

An orientation to the binomial distribution: what you will learn, how it builds on expected value, the math involved, and the time and difficulty to expect.

An orientation to summarizing data before modeling: the two questions every summary answers, prerequisites, the math involved, and what the lesson covers.

How the capstone maps statistics tools onto an ML project, tests a model claim with four questions, and bounds where the statistical-thinking layer ends.

Overview of the sampling and central limit theorem lesson: what it covers, the prerequisites, the light math, and the skills you will build before you start.

Preview of the random variables and expected value lesson: scope, prerequisites, the arithmetic involved, and how expected value underpins machine learning.

An orientation to the probability lesson: what you will learn, where it fits in the track, prerequisites, and the math and time to expect before you start.

An orientation to the hypothesis testing lesson: the null and alternative, the p-value, prerequisites, and the misreadings that make p so abused.

An overview of the confidence interval lesson: how to build the interval, what sets its width, the correct interpretation, and how to read AI metrics with it.

Overview of the conditional probability lesson: what you will learn, how it fits after the multiplication rule, the prerequisites, and the math involved.

Overview of the Bayes' theorem lesson: what you will learn, prerequisites, the natural-frequencies and formula approach, and how it connects to AI.

Overview of the datasets-library lesson: what you will learn, where it fits, prerequisites, and time to load, clean, and transform real data.

What the tokenizers lesson covers: the four-stage pipeline, fast vs slow tokenizers, the three subword algorithms, and training one on a corpus.

Orientation for the common NLP tasks lesson: the shared loop, how each task maps to a head and metric, prerequisites, and what to read next.

An overview of publishing to the Hugging Face Hub: authenticate, compare the three upload routes, and learn why the model card is the real deliverable.

What this code lesson covers: the pipeline() one-liner, the three steps it hides, the Auto classes, logits, and the from_pretrained idiom.

Overview of the reasoning-models capstone: what they add over LLMs, how RL trains step-by-step thinking, and the working method that outlasts the frontier.